Prevalence of Latent Tuberculosis Infection Among Persons with Chronic Hepatitis B Virus Infection: A Systematic Review and Meta-Analysis

Expected vs Reported Chronic Hepatitis B Infection Cases in Persons with Active Tuberculosis - California, 2016-2020

Epidemiologic data regarding persons with active tuberculosis (TB) and chronic hepatitis B virus (cHBV) infection are limited because of lack of routine surveillance of cHBV in persons with TB. Potential underdiagnosis of cHBV in California among those with TB is concerning. We matched TB and cHBV registries to identify cHBV infections among persons diagnosed with TB during 2016-2020 and described their demographic characteristics. We calculated expected cHBV cases among persons with TB for each demographic characteristic using published cHBV prevalence estimates for the locations of birth for persons with TB. Estimates were from general or emigrant adult and teen populations. Reported cHBV infection among persons with TB were 23% lower than expected, particularly among Asian persons, persons living in the two healthiest Healthy Places Index quartiles, and residents of less populated jurisdictions in California. Results show the possibility exists for underdiagnosis of cHBV in persons with TB in California.

Epidemiology and Treatment Outcomes of Tuberculosis With Chronic Hepatitis B Infection-California, 2016-2020

BACKGROUND

Improved epidemiologic and treatment data for active tuberculosis (TB) with chronic hepatitis B virus (cHBV) infection might inform and encourage screening and vaccination programs focused on persons at risk of having both conditions.

METHODS

We matched the California Department of Public Health TB registry during 2016-2020 to the cHBV registry using probabilistic matching algorithms. We used chi-square analysis to compare the characteristics of persons with TB and cHBV with those with TB only. We compared TB treatment outcomes between these groups using modified Poisson regression models. We calculated the time between reporting of TB and cHBV diagnoses for those with both conditions.

RESULTS

We identified 8435 persons with TB, including 316 (3.7%) with cHBV. Among persons with TB and cHBV, 256 (81.0%) were non-US-born Asian versus 4186 (51.6%) with TB only (P < .0001). End-stage renal disease (26 [8.2%] vs 322 [4.0%]; P < .001) and HIV (21 [6.7%] vs 247 [3.0%]; P = .02) were more frequent among those with TB and cHBV compared with those with TB only. Among those with both conditions, 35 (11.1%) had TB diagnosed >60 days before cHBV (median, 363 days) and 220 (69.6%) had TB diagnosed >60 days after cHBV (median, 3411 days).

CONCLUSIONS

Persons with TB and cHBV were found more frequently in certain groups compared with TB only, and infrequently had their conditions diagnosed together. This highlights an opportunity to improve screening and treatment of TB and cHBV in those at high risk for coinfection.

Latent Tuberculosis Infection Testing Practices in a Large US Integrated Healthcare System

BACKGROUND

Tuberculosis (TB) is a public health threat, with >80% of active TB in the United States occurring due to reactivation of latent TB infection (LTBI). We may be underscreening those with high risk for LTBI and overtesting those at lower risk. A better understanding of gaps in current LTBI testing practices in relation to LTBI test positivity is needed.

METHODS

This study, conducted between 1 January 2008 and 31 December 2019 at Kaiser Permanente Southern California, included individuals aged ≥18 years without a history of active TB. We examined factors associated with LTBI testing and LTBI positivity.

RESULTS

Among 3 816 884 adults (52% female, 37% White, 37% Hispanic, mean age 43.5 years [standard deviation, 16.1]), 706 367 (19%) were tested for LTBI, among whom 60 393 (9%) had ≥1 positive result. Among 1 211 971 individuals who met ≥1 screening criteria for LTBI, 210 025 (17%) were tested for LTBI. Factors associated with higher adjusted odds of testing positive included male sex (1.32; 95% confidence interval, 1.30-1.35), Asian/Pacific Islander (2.78, 2.68-2.88), current smoking (1.24, 1.20-1.28), diabetes (1.13, 1.09-1.16), hepatitis B (1.45, 1.34-1.57), hepatitis C (1.54, 1.44-1.66), and birth in a country with an elevated TB rate (3.40, 3.31-3.49). Despite being risk factors for testing positive for LTBI, none of these factors were associated with higher odds of LTBI testing.

CONCLUSIONS

Current LTBI testing practices may be missing individuals at high risk of LTBI. Additional work is needed to refine and implement screening guidelines that appropriately target testing for those at highest risk for LTBI.

A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study

Objective

According to the Global Tuberculosis Report for three consecutive years, tuberculosis (TB) is the second leading infectious killer. Primary pulmonary tuberculosis (PTB) leads to the highest mortality among TB diseases. Regretfully, no previous studies targeted the PTB of a specific type or in a specific course, so models established in previous studies cannot be accurately feasible for clinical treatments. This study aimed to construct a nomogram prognostic model to quickly recognize death-related risk factors in patients initially diagnosed with PTB to intervene and treat high-risk patients as early as possible in the clinic to reduce mortality.

Methods

We retrospectively analyzed the clinical data of 1,809 in-hospital patients initially diagnosed with primary PTB at Hunan Chest Hospital from January 1, 2019, to December 31, 2019. Binary logistic regression analysis was used to identify the risk factors. A nomogram prognostic model for mortality prediction was constructed using R software and was validated using a validation set.

Results

Univariate and multivariate logistic regression analyses revealed that drinking, hepatitis B virus (HBV), body mass index (BMI), age, albumin (ALB), and hemoglobin (Hb) were six independent predictors of death in in-hospital patients initially diagnosed with primary PTB. Based on these predictors, a nomogram prognostic model was established with high prediction accuracy, of which the area under the curve (AUC) was 0.881 (95% confidence interval [Cl]: 0.777-0.847), the sensitivity was 84.7%, and the specificity was 77.7%.Internal and external validations confirmed that the constructed model fit the real situation well.

Conclusion

The constructed nomogram prognostic model can recognize risk factors and accurately predict the mortality of patients initially diagnosed with primary PTB. This is expected to guide early clinical intervention and treatment for high-risk patients.